Functional annotation and identification of candidate disease genes by computational analisys of normal tissue gene expression data (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• Functional annotation and identification of candidate disease genes by computational analisys of normal tissue gene expression data (Articolo in rivista) (literal)

Anno

• 2008-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1371/journal.pone.0002439 (literal)

Alternative label

• Miozzi, L., Piro, R.M., Rosa, F., Ala, U., Silengo, L., Di Cunto, F., Provero, P. (2008)
  Functional annotation and identification of candidate disease genes by computational analisys of normal tissue gene expression data
  in PloS one
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Miozzi, L., Piro, R.M., Rosa, F., Ala, U., Silengo, L., Di Cunto, F., Provero, P. (literal)

Pagina inizio

• 2439 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni

• Il progetto è stato svolto con il supporto della Fondazione ISI, Progetto Lagrange. (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

• http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0002439 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 3 (literal)

Rivista

• PloS one (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

• 6 (literal)

Note

• ISI Web of Science (WOS) (literal)
• PubMe (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• ML Institute of Plant Virology, CNR, Turin, Italy, PRM, AU, SL, DCF, PP Molecular Biotechnology Center and Department of Genetics, Biology and Biochemistry, University of Turin, Torino, Italy, RF ISI Foundation, Turin, Italy (literal)

Titolo

• Functional annotation and identification of candidate disease genes by computational analisys of normal tissue gene expression data (literal)

Abstract

• BACKGROUND: High-throughput gene expression data can predict gene function through the \"guilt by association\" principle: coexpressed genes are likely to be functionally associated. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed publicly available expression data on normal human tissues. The analysis is based on the integration of data obtained with two experimental platforms (microarrays and SAGE) and of various measures of dissimilarity between expression profiles. The building blocks of the procedure are the Ranked Coexpression Groups (RCG), small sets of tightly coexpressed genes which are analyzed in terms of functional annotation. Functionally characterized RCGs are selected by means of the majority rule and used to predict new functional annotations. Functionally characterized RCGs are enriched in groups of genes associated to similar phenotypes. We exploit this fact to find new candidate disease genes for many OMIM phenotypes of unknown molecular origin. CONCLUSIONS/SIGNIFICANCE: We predict new functional annotations for many human genes, showing that the integration of different data sets and coexpression measures significantly improves the scope of the results. Combining gene expression data, functional annotation and known phenotype-gene associations we provide candidate genes for several genetic diseases of unknown molecular basis. (literal)

Autore CNR

• LAURA MIOZZI (Unità di personale interno)

Insieme di parole chiave

• Parole chiave di "Functional annotation and identification of candidate disease genes by computational analisys of normal tissue gene expression data" (Insieme di parole chiave)

Incoming links:

Autore CNR di

• LAURA MIOZZI (Unità di personale interno)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• PloS one (Rivista)

Insieme di parole chiave di

• Parole chiave di "Functional annotation and identification of candidate disease genes by computational analisys of normal tissue gene expression data" (Insieme di parole chiave)